Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50016631" target="_blank" >RIV/62690094:18450/19:50016631 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8987044" target="_blank" >http://dx.doi.org/10.1109/ICBDA47563.2019.8987044</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8987044" target="_blank" >10.1109/ICBDA47563.2019.8987044</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

Popis výsledku v původním jazyce

This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative Clustering and K-means Clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data.

Název v anglickém jazyce

Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

Popis výsledku anglicky

This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative Clustering and K-means Clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2019 IEEE Conference on Big Data and Analytics, ICBDA 2019

ISBN

978-1-72813-308-9

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

34-40

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

US, piscataway

Místo konání akce

Penang, Malaysia

Datum konání akce

19. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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